Key points are not available for this paper at this time.
Sandy soils in Egypt's newly reclaimed lands face multiple challenges due to their low water-holding capacity, nutrient leaching, and high evapotranspiration, all of which threaten sustainable crop production. This study evaluated an artificial intelligence-driven decision support system (AI-DSS) for managing irrigation and fertilization in wheat ( Triticum aestivum L.), maize ( Zea mays L.), and sugar beet ( Beta vulgaris L.) over three consecutive seasons (2022–2025). The AI-DSS integrated real-time soil moisture, nutrient, and weather data using Random Forest and LSTM models to optimize input scheduling. Compared to conventional farmer practices (CFP), AI-DSS led to yield increases of up to 13.1 % and improvements in water use efficiency (WUE) by up to 15.5 %, particularly in sugar beet during 2024. Partial factor productivity (PFP) also increased significantly, especially in maize. Post-harvest soil analysis indicated higher residual levels of nitrogen (+13.6–19.3 %), phosphorus (+22.7–25.0 %), and organic matter (+17.9–22.0 %), along with a 13–19 % reduction in soil salinity. Economic assessments showed an 8.5–15.0 % increase in the benefit–cost ratio (BCR). Additionally, nitrate leaching was substantially reduced under AI-DSS, mitigating environmental risks. These results underscore the potential of AI-driven management to enhance productivity, input-use efficiency, and soil sustainability in coarse-textured soils of arid regions. • AI-DSS improved crop yields by up to 13.1 % in sandy soils of arid Egypt. • WUE rose by 15.5 %, notably in sugar beet. • In maize, AI-driven scheduling boosted PFP by 28 %. • Nitrate leaching decreased by 40–45 %, reducing environmental risk. • Soil N, P, and OM improved post-harvest.
EMARA et al. (Sun,) studied this question.